RESEARCH & RESOURCES

Customer Data Integration: Five Areas for Improvement

We explore five areas where CDI has the most room for improvement -- and opportunity for greater benefit -- and recommend corrective actions that user organizations can apply successfully.

By Philip Russom, Ph.D.

February 11, 2010

Business and technology users regularly tell
me how useful customer data integration (CDI) is for
improving customer service, the 360-degree view of each
customer, and sales and marketing activities. Yet, these
same users lament the regrettable condition that their CDI
solutions have deteriorated into over the years. That's
because most CDI solutions today are homegrown legacies
that lack modern technologies such as data quality and
master data management. Many organizations suffer numerous
CDI solutions that are siloed and redundant, sometimes
contradictory and non-compliant. Aggregated customer data
is all too often incomplete, poorly modeled, and myopic.

It's no wonder that half of users responding to a TDWI
survey consider their organization's CDI success to be
mediocre. Yet, CDI's success could be greatly enhanced if
user organizations enhanced their CDI solutions in the
right ways. Allow me to point out the top five areas where
CDI has the most room for improvement -- and opportunity
for greater benefit -- and recommend corrective actions
that I've seen user organizations apply successfully.

1. Consolidate or Federate Disparate CDI
Solutions

Survey data from TDWI Research shows that user
organizations have 5.2 CDI solutions on average. That's
actually a very manageable number of systems. The number
isn't the problem; it's the fact that the solutions are
owned by diverse departments and are connected to a short
list of systems, such that each is a silo -- the very thing
that CDI is supposed to cure!

If you're serious about 360-degree views of customers
and data consistency, you'll consolidate redundant CDI
solutions and federate or otherwise integrate the ones you
can't consolidate. If possible, such corrections should be
in the context of a larger plan for evolving CDI from a
point solution to an enterprisewide practice that treats
customer data as an enterprise asset instead of a
departmental commodity.

2. Update or Replace Legacy CDI
Solutions

Some solutions are 15 or even 20 years old. By now, all
these are legacies that need to be replaced or modernized
significantly. After all, we know a lot more today about
integrating and modeling customer data than we did back
then, and there are many more data management tools
available that you could apply to CDI.

A compelling reason for an update or replacement is to
satisfy new technology requirements for data integration,
data quality, master data management, and data governance.
Other modern techniques to consider include service-
oriented architecture, real-time interoperability, and
bidirectional data synchronization. Without these, it's
hard for your CDI solution to stay relevant as your
business becomes more digital.

3. Close the Loop with Integrated Customer
Data

Almost all home-grown CDI solutions integrate customer
data one-way into a mid-tier database but rarely push the
data back upstream to operational applications where most
of it came from. Ideally, the improved and completed
customer data of the CDI solution should close the loop
back to source systems, so they and their users benefit,
too.

4. Revise Your Data Models for Better
Analytics

Customer data is usually modeled in flat tabular
structures. This is fine for CDI solutions that support
front-office operational activities such as direct
marketing, customer service, and sales. However, as more
firms move deeper into analytics, they need customer data
aggregated and modeled in hierarchical or multidimensional
views for analytic tasks, such as customer segmentation,
propensity to churn, and customer profitability.

5. Make Aggregated Data as Complete as
Possible

You may need to alter business processes (especially for
sales and service) to capture more data about the customer
at each touch point. Silo'd CDI solutions take a
departmental view, whereas the consolidated systems
mentioned earlier yield a complete enterprise view.

Furthermore, internal data tells you only what the
customer does with your organization; to find out what the
customer does elsewhere, you need to acquire third-party
customer data from an external provider.

Philip Russom, Ph.D., is senior director of TDWI Research for data management and is a well-known figure in data warehousing, integration, and quality, having published over 600 research reports, magazine articles, opinion columns, and speeches over a 20-year period. Before joining TDWI in 2005, Russom was an industry analyst covering data management at Forrester Research and Giga Information Group. He also ran his own business as an independent industry analyst and consultant, was a contributing editor with leading IT magazines, and a product manager at database vendors. His Ph.D. is from Yale. You can reach him by email (prussom@tdwi.org), on Twitter (twitter.com/prussom), and on LinkedIn (linkedin.com/in/philiprussom).